As a Lead Data Scientist at GeBBS, you will drive NLP and machine learning projects and be responsible for developing methodology and solutions to support technical, analytical, and operational requirements.
Requirements
- 6+ years of experience working in the data science field, preferably in a product development environment, with a focus on building NLP/LLMs or Deep Learning models.
- 2+ years of experience in managing data science teams as a lead or a mentor
- Strong programming skills in Python or other relevant programming languages.
- Experience with machine learning libraries (e.g., sci-kit-learn, TensorFlow, PyTorch).
- Deep understanding of statistical analysis, probability theory, and experimental design.
- Experience with LLMs, deep learning, NLP, and chatbot development.
- Excellent communication skills, including the ability to explain complex concepts to technical and non-technical stakeholders.
- Experience working with a variety of statistical models, including logistic regression, clustering, classification, SVMs, neural networks, Random Forest, CRF, Bayesian models, supervised/unsupervised learning, etc.
- Expertise in NLP techniques, including sentiment analysis, word embedding, part-of-speech (POS) tagging, topic modeling, text classification, machine translation, speech recognition, named entity recognition (NER), natural language generation (NLG), and other related techniques.
- Experience with various deep learning techniques, including CNNs and RNNs, and a strong understanding of building and training these models for different applications. Familiarity with LMs and LLMs such as GPT, BERT, and Transformer models is highly desirable.
- Strong ability to rapidly comprehend and implement research papers related to AI, as well as remain informed of the latest advancements in NLP technologies.
- Deep knowledge and experience in structured and unstructured data Information Extraction, Knowledge Information Retrieval, and Knowledge Representation.
- Self-motivated and driven to satisfy intellectual curiosity through the pursuit of continuous learning and skill development.
- Strong problem-solving and analytical skills.
- Optional: Experience with large-scale data processing technologies (e.g., Hadoop, Spark) and distributed computing systems.
